By CPA Abdhalla Mambo
Making Data-Driven Decisions Can Lead To Better Business Outcomes
Change is hard. Never has this been truer as we collectively navigate major social changes across the globe. As humans, we find comfort in routine and familiarity, while sometimes not seeing how embracing new challenges helps us evolve and grow. This is especially true when it comes to business. Change can be internal to the business or due to external factors.
We often get so caught up in our day-to-day responsibilities that we don’t recognize that something needs to be done in order to meet these new challenges. It usually requires someone or something to push us out of our comfort zone and force us to recognize that change is occurring.This is especially true when it comes to business.
Change can be internal to the business or due to external factors. We often get so caught up in our day-to-day responsibilities that we don’t recognize that something needs to be done in order to meet these new challenges. It usually requires someone or something to push us out of our comfort zone and force us to recognize that change is occurring.
It is no secret that making data-driven decisions can lead to better business outcomes, help manage resources, boost operational efficiency, and even navigate an uncertain economic environment. Ideally, an organization has widely democratized, robust processes where everyone can tap into analytics insights.
So, what is analytics maturity anyway?
Analytics maturity is the measure of a company’s analytics proficiency. By evaluating analytics maturity, business leaders can observe their organization’s current usage of data to strategically plan upskilling programs or investments that need to be made to further advance data literacy and analytics usage within their organizations. As with many things in life, you need to know where you are to know where you need to go.
The Data and Analytics Maturity Model Organizations rarely are at the same level of maturity for all capabilities. Data and analytics maturity model is a tool to chart a path toward improving overall organizational capability. It’s more common to span different levels of maturity according to your
The first capability is related to data infrastructure and how it is managed. In less mature organizations, data can be siloed across multiple locations and formats (i.e., data marts, SaaS applications, operational databases and raw files). More mature organizations have moved toward centralized cloud data platforms and are leveraging data sources beyond their own applications.
Next is access. Less mature organizations often leave users to wrangle their own data with one-off imports or extracts. More mature organizations create data pipelines using Extract Transform and Load (ETL) or Extract, Load and Transform (ELT) data integration approaches to move data to a central location, pre-positioning it for users to access.
The most mature organizations may incorporate data virtualization to provide live access to data without
complex data movement and give users the choice of data access best suited to their particular use case.
Data modeling is about making data consumable by ordinary humans. Less mature organizations ignore data models and default to building one off datasets that too often produce inconsistent results. Most organizations rely on tabular data modeling with data managed within data warehouse tables. The tabular approach provides some standardization but still leaves the interpretation of raw data up to the data consumers.
The most mature organizations define a logical, dimensional model of their data to expose a consistent, business-friendly interface of key metrics to data consumers, making data accessible to a wider range of users.
Data consumption refers to how users query data and how they share insights with others. For less mature organizations, custom Structured Query Language (SQL) and code are the primary tools for asking questions of data, limiting access to a small set of power users. More mature organizations provide “data as a service” to allow users to explore and report on business metrics using tools of their choice.
The most mature organizations deploy “data as code” by embedding analytics inside applications and business operations to move beyond the dashboard.
Insights relate to how data and analytic teams empower better decision-making and the creation of new business value through the delivery of data-driven insights. Less mature organizations focus only on historical data. More mature organizations produce insights that predict future results (i.e., predicted
sales and inventory) and prescribe actions to capture opportunities.
Stages of analytics maturity
Analytics maturity models describe the progressive path that analytics take from being just an activity to becoming a critical component of business strategy. We can break down this path into five key steps:
i. No analytics. This is the initial stage of the data analytics maturity model. It may refer to emerging startups or companies that overlooked analytics processes at some point.
ii. Descriptive analytics. This stage enables an understanding of the reality and current events through
the depiction of data. Descriptive analytics answers the question: “What happened?” Analysts use it
to measure the effectiveness of the organization’s efforts.
iii. Diagnostic analytics. Diagnostic analytics detects relationships between different variables through
the analysis of historical data. It answers the question: “Why did it happen?”
iv. Predictive analytics. This stage is the frontier of advanced analytics. Predictive analysts create detailed forecasts and foresee the outcomes of actions, events, and trends. With predictive analytics, companies can answer the question: “What will happen in the future?” A predictive system helps organizations make informed decisions by analyzing their previous actions.
v. Prescriptive analytics. Prescriptive analytics is the top level of analytics that every company should
seek. It implements machine learning algorithms to make recommendations on further actions. In a nutshell, at this stage, you get the answer to the question: “What actions should be taken?”
Where is your organization on its analytics maturity journey?
To identify where your organization is on its journey, you need to understand the four dimensions of analytics maturity.
Data maturity: To have a successful analytics strategy, companies must, of course, have a certain level of
data maturity. This refers to how a company collects data, the quality of the data, where it is stored, and how it is used.
Organizational dynamics: Analytically mature companies need to have strong operational health.
In other words, they need to have solid internal processes for data collection, software implementation,
training employees, and making sure the right teams have what they need to drive data strategy forward.
Analytics team dynamics: In any department, team chemistry is king. So why would analytics be any
different? It is crucial for data and analytics teams to work efficiently and effectively with each other as
well as the key stakeholders within the company.
Usage and technology: As digital transformation stays at the forefront of company priorities across industries, it is important to ensure that employees across an organization have access to the appropriate tools and training to make data-driven decisions.
Improving your organization analytics maturity
Once you understand where your organization falls within each bucket, you can make an action plan to make sure your company stays on track to successfully execute its data strategy. It is obvious that analytics plays a key role in decision-making and a company’s overall development. Here are a few
tips to strengthen each dimension of analytics maturity.
- Meet employees where they are. In the age of the digital revolution, organizations need to ensure employees can securely access data wherever they need it, whether that be on premises or in the cloud. To achieve data maturity, companies should also keep accurate records of what data is being collected and how it is being used.
- Process makes perfect. To improve organizational dynamics, companies should implement processes for data collection that are introduced during employee onboarding. Of course,
data collection looks different across the departments, but it is important to have a consistent framework for policies around data collection.
- Teamwork makes the dream work: As cliché as it is, there is no shortcut to building cohesive teams. Team dynamics and morale trickle down from the top of an organization, so analytics leaders should strive to promote a culture of communication within their teams.
- Analytics teams must work in lockstep with other key stakeholders across the companyto successfully execute an analytics strategy. Analytics for all: Business leaders should look for no code/codefriendly analytics solutions to help democratize insights for nontechnical employees. And it doesn’t stop there.
Organizations should prioritize training and upskilling programs for all employees, not just data workers, so individuals across every department can start making data-driven decisions faster. Furthermore, when
looking for outstanding candidates in the hiring process, selecting those with data literacy and/or analytics experience can be beneficial to just about any department, but especially Finance, HR, and Operations.
Improving the maturity index of a company will not be an easy task, since competing on analytics requires
fundamental changes across the entire organization. Companies must create a data-driven culture, leaders need to develop new skills, legacy processes need to be changed and organizational inertia must be overcome.
This transformational process might take a company months or even years to complete, but in the end, it will bring economic benefits and operational efficiencies that will be worth the effort to gain a competitive advantage against their competitors.